Background. Acute ST-segment elevation myocardial infarction (STEMI) is a serious cardiovascular disease that poses a great threat to the life and health of patients. Therefore, early diagnosis is important for STEMI patient treatment and prognosis. The purpose of this study was to investigate the value of serum YKL-40 and TNF-α in the diagnosis of STEMI. Methods. From October 2020 to February 2022, 120 patients with STEMI were admitted to the Chest Pain Center of the Second People’s Hospital of Hefei, and 81 patients with negative coronary angiography were selected as the control group. Serum YKL-40 and TNF-α concentrations were measured by sandwich ELISA. Pearson correlation was used to analyze the correlation between serum YKL-40, TNF-α, and serum troponin I (cTnI) in STEMI patients; multivariate logistic regression analysis was used to screen independent risk factors for STEMI. Three diagnostic models were constructed: cTnI univariate model (model A), combined serum YKL-40 and TNF-α model other than cTnI (model B), and combined cTnI and serum YKL-40 and TNF-α model (model C). We assessed the clinical usefulness of the diagnostic model by comparing AUC with decision curve analysis (DCA). Results. Serum YKL-40 and TNF-α in the STEMI group were significantly higher than those in the control group ( P < 0.001 ). On Pearson correlation analysis, there was a significant positive correlation between serum YKL-40, TNF-α, and cTnI levels in STEMI patients. Multivariate logistic regression analysis showed that serum YKL-40 and TNF-α were independent risk factors for the development of STEMI. The results of ROC analysis showed that the area under the curve (AUC) of serum YKL-40 for predicting the occurrence of STEMI was 0.704. The AUC of serum TNF-α for predicting the occurrence of STEMI was 0.852. The AUC of cTnI as a traditional model, model A, for predicting the occurrence of STEMI was 0.875. Model B predicted STEMI with an AUC of 0.851. The addition of serum YKL-40 and serum TNF-α to the traditional diagnostic model composed of cTnI constituted a new diagnostic model; that is, the AUC of model C for predicting the occurrence of STEMI was 0.930. Model C had a better net benefit between a threshold probability of 70–95% for DCA. Conclusion. In this study, we demonstrate the utility of serum YKL-40 and TNF-α as diagnostic markers for STEMI and the clinical utility of diagnostic models by combining serum YKL-40 and TNF-α with cTnI.

BackgroundEmergency percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) helps to reduce the occurrence of major adverse cardiovascular events (MACEs) such as death, cardiogenic shock, and malignant arrhythmia, but in-hospital MACEs may still occur after emergency PCI, and their mortality is significantly increased once they occur. The aim of this study was to investigate the risk factors associated with MACE during hospitalization after PCI in STEMI patients, construct a nomogram prediction model and evaluate its effectiveness.MethodsA retrospective analysis of 466 STEMI patients admitted to our hospital from January 2018 to June 2022. According to the occurrence of MACE during hospitalization, they were divided into MACE group (n = 127) and non-MACE group (n = 339), and the clinical data of the two groups were compared; least absolute shrinkage and selection operator (LASSO) regression was used to screen out the predictors with non-zero coefficients, and multivariate Logistic regression was used to analyze STEMI Independent risk factors for in-hospital MACE in patients after emergency PCI; a nomogram model for predicting the risk of in-hospital MACE in STEMI patients after PCI was constructed based on predictive factors, and the C-index was used to evaluate the predictive performance of the prediction model; the Bootstrap method was used to repeat sampling 1,000 Internal validation was carried out for the second time, the Hosmer-Lemeshow test was used to evaluate the model fit, and the calibration curve was drawn to evaluate the calibration degree of the model. Receiver operating characteristic (ROC) curves were drawn to evaluate the efficacy of the nomogram model and thrombolysis in myocardial infarction (TIMI) score in predicting in-hospital MACE in STEMI patients after acute PCI.ResultsThe results of LASSO regression showed that systolic blood pressure, diastolic blood pressure, Killip grade II-IV, urea nitrogen and left ventricular ejection fraction (LVEF), IABP, NT-ProBNP were important predictors with non-zero coefficients, and multivariate logistic regression analysis was performed to analyze that Killip grade II-IV, urea nitrogen, LVEF, and NT-ProBNP were independent factors for in-hospital MACE after PCI in STEMI patients; a nomogram model for predicting the risk of in-hospital MACE after PCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.826 (95% CI: 0.785–0.868) having a good predictive power; the results of H-L goodness of fit test showed χ2 = 1.3328, P = 0.25, the model calibration curve was close to the ideal model, and the internal validation C-index was 0.818; clinical decision analysis also showed that the nomogram model had a good clinical efficacy, especially when the threshold probability was 0.1–0.99, the nomogram model could bring clinical net benefits to patients. The nomogram model predicted a greater AUC (0.826) than the TIMI score (0.696) for in-hospital MACE after PCI in STEMI patients.ConclusionUrea nitrogen, Killip class II-IV, LVEF, and NT-ProBNP are independent factors for in-hospital MACE after PCI in STEMI patients, and nomogram models constructed based on the above factors have high predictive efficacy and feasibility.

Background. Acute ST-elevation myocardial infarction (STEMI) is a common clinical critical illness, and accurate, reliable, simple, and easy-to-remember tools are needed in clinical practice to quickly identify the risk of this condition in STEMI patients. This study investigates the predictive value of the admission CHA2DS2-VASc score for in-hospital MACE in STEMI patients. Methods. A total of 210 STEMI patients who visited the Chest Pain Center of the Second People‘s Hospital of Hefei from December 2019 to December 2021 were retrospectively analyzed. They were divided into MACE and non-MACE groups. The receiver operating characteristic curve (ROC) was used to assess the predictive value of the CHA2DS2-VASc score for MACE events during hospitalization. Results. The CHA2DS2-VASc score was higher in the MACE group than in the non-MACE group ( P < 0.05 ), and multivariate logistic regression analysis showed that the CHA2DS2-VASc score was an independent risk factor for MACE events during hospitalization in STEMI patients (OR = 1.391, 95%CI 1.044–1.853, P = 0.024 ); ROC curve analysis showed that the area under the curve (AUC) of the CHA2DS2-VASc score was 0.744, the sensitivity was 0.64, the specificity was 0.694, and the optimal cutoff value was 3.5 in predicting the risk of MACE events during hospitalization in STEMI patients. There were no significant differences between the GRACE score (0.744 VS.0.827) and TIMI score (0.744VS.0.745) ( P > 0.05 ). Conclusion. The CHA2DS2-VASc score can successfully predict the occurrence of in-hospital MACE events in STEMI patients.